no code implementations • 19 Mar 2024 • Raphael Norman-Tenazas, David Kleinberg, Erik C. Johnson, Daniel P. Lathrop, Matthew J. Roos
In one type of implementation the output nodes are used directly to perform a task and all learning is via evolution of the network's node functions.
no code implementations • 26 May 2023 • Erik C. Johnson, Brian S. Robinson, Gautam K. Vallabha, Justin Joyce, Jordan K. Matelsky, Raphael Norman-Tenazas, Isaac Western, Marisel Villafañe-Delgado, Martha Cervantes, Michael S. Robinette, Arun V. Reddy, Lindsey Kitchell, Patricia K. Rivlin, Elizabeth P. Reilly, Nathan Drenkow, Matthew J. Roos, I-Jeng Wang, Brock A. Wester, William R. Gray-Roncal, Joan A. Hoffmann
We envision a pipeline to utilize large neuroimaging datasets, including maps of the brain which capture neuron and synapse connectivity, to improve machine learning approaches.
1 code implementation • 24 Feb 2020 • Matthew J. Roos
Owing to this paradigm, learned decision spaces for individual classes span excessively large regions of the input space and include images that have no semantic similarity to images in the training set.